Multiple Surface-Based Biosensors for Enhanced Molecular Detection in Fluid Flow Systems
Bibliographic record
Abstract
This paper addresses the problem of detecting minute concentrations (nano to pico-molar) of analyte in a fluid flow chamber using an array of surface-based sensors. It is shown that in the mass-transport influenced case, when the rate of transport of analyte is comparable to or smaller than the intrinsic reaction rates at the sensor surface, substantial improvements in the response rate can be obtained from an array of spaced small sensor surfaces relative to a single large surface. Advection-diffusion-reaction models are developed to predict the response of such sensor arrays, which are compared to individual sensor surfaces of the same total area. Formulas are derived for quantifying the improvement in performance and optimal size of the sensors in the array. The results of the model are compared with experimental data obtained for an ion-channel switch biosensor and a surface plasmon resonance biosensor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".